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  <div class="section" id="module-pybrain.structure.modules">
<h1><tt class="xref docutils literal"><span class="pre">modules</span></tt> &#8211; Structural Components: Modules<a class="headerlink" href="#module-pybrain.structure.modules" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="pybrain.structure.modules.BiasUnit">
<em class="property">class </em><tt class="descclassname">pybrain.structure.modules.</tt><tt class="descname">BiasUnit</tt><big>(</big><em>name=None</em><big>)</big><a class="headerlink" href="#pybrain.structure.modules.BiasUnit" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <tt class="xref docutils literal"><span class="pre">pybrain.structure.modules.neuronlayer.NeuronLayer</span></tt></p>
<p>A simple bias unit with a single constant output.</p>
</dd></dl>

<dl class="class">
<dt id="pybrain.structure.modules.GaussianLayer">
<em class="property">class </em><tt class="descclassname">pybrain.structure.modules.</tt><tt class="descname">GaussianLayer</tt><big>(</big><em>dim</em>, <em>name=None</em><big>)</big><a class="headerlink" href="#pybrain.structure.modules.GaussianLayer" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <tt class="xref docutils literal"><span class="pre">pybrain.structure.modules.neuronlayer.NeuronLayer</span></tt>, <tt class="xref docutils literal"><span class="pre">pybrain.structure.parametercontainer.ParameterContainer</span></tt></p>
<p>A layer implementing a gaussian interpretation of the input. The mean is
the input, the sigmas are stored in the module parameters.</p>
<dl class="method">
<dt id="pybrain.structure.modules.GaussianLayer.setSigma">
<tt class="descname">setSigma</tt><big>(</big><em>sigma</em><big>)</big><a class="headerlink" href="#pybrain.structure.modules.GaussianLayer.setSigma" title="Permalink to this definition">¶</a></dt>
<dd>Wrapper method to set the sigmas (the parameters of the module) to a
certain value.</dd></dl>

</dd></dl>

<dl class="class">
<dt id="pybrain.structure.modules.LinearLayer">
<em class="property">class </em><tt class="descclassname">pybrain.structure.modules.</tt><tt class="descname">LinearLayer</tt><big>(</big><em>dim</em>, <em>name=None</em><big>)</big><a class="headerlink" href="#pybrain.structure.modules.LinearLayer" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <tt class="xref docutils literal"><span class="pre">pybrain.structure.modules.neuronlayer.NeuronLayer</span></tt></p>
<p>The simplest kind of module, not doing any transformation.</p>
<dl class="method">
<dt id="pybrain.structure.modules.LinearLayer.__init__">
<tt class="descname">__init__</tt><big>(</big><em>dim</em>, <em>name=None</em><big>)</big><a class="headerlink" href="#pybrain.structure.modules.LinearLayer.__init__" title="Permalink to this definition">¶</a></dt>
<dd>Create a layer with dim number of units.</dd></dl>

</dd></dl>

<dl class="class">
<dt id="pybrain.structure.modules.LSTMLayer">
<em class="property">class </em><tt class="descclassname">pybrain.structure.modules.</tt><tt class="descname">LSTMLayer</tt><big>(</big><em>dim</em>, <em>peepholes=False</em>, <em>name=None</em><big>)</big><a class="headerlink" href="#pybrain.structure.modules.LSTMLayer" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <tt class="xref docutils literal"><span class="pre">pybrain.structure.modules.neuronlayer.NeuronLayer</span></tt>, <tt class="xref docutils literal"><span class="pre">pybrain.structure.parametercontainer.ParameterContainer</span></tt></p>
<p>Long short-term memory cell layer.</p>
<p>The input consists of 4 parts, in the following order:
- input gate
- forget gate
- cell input
- output gate</p>
<dl class="method">
<dt id="pybrain.structure.modules.LSTMLayer.__init__">
<tt class="descname">__init__</tt><big>(</big><em>dim</em>, <em>peepholes=False</em>, <em>name=None</em><big>)</big><a class="headerlink" href="#pybrain.structure.modules.LSTMLayer.__init__" title="Permalink to this definition">¶</a></dt>
<dd><table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field"><th class="field-name">Parameter:</th><td class="field-body"><em>dim</em> &#8211; number of cells</td>
</tr>
<tr class="field"><th class="field-name">Key peepholes:</th><td class="field-body">enable peephole connections (from state to gates)?</td>
</tr>
</tbody>
</table>
</dd></dl>

</dd></dl>

<dl class="class">
<dt id="pybrain.structure.modules.MDLSTMLayer">
<em class="property">class </em><tt class="descclassname">pybrain.structure.modules.</tt><tt class="descname">MDLSTMLayer</tt><big>(</big><em>dim</em>, <em>dimensions=1</em>, <em>peepholes=False</em>, <em>name=None</em><big>)</big><a class="headerlink" href="#pybrain.structure.modules.MDLSTMLayer" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <tt class="xref docutils literal"><span class="pre">pybrain.structure.modules.neuronlayer.NeuronLayer</span></tt>, <tt class="xref docutils literal"><span class="pre">pybrain.structure.parametercontainer.ParameterContainer</span></tt></p>
<p>Multi-dimensional long short-term memory cell layer.</p>
<p>The cell-states are explicitly passed on through a part of 
the input/output buffers (which should be connected correctly with IdentityConnections).</p>
<p>The input consists of 4 parts, in the following order:
- input gate
- forget gates (1 per dim)
- cell input
- output gate
- previous states (1 per dim)</p>
<p>The output consists of two parts:
- cell output
- current statte</p>
<p>Attention: this module has to be used with care: it&#8217;s last &lt;size&gt; input and
outputs are reserved for transmitting internal states on flattened recursive
multi-dim networks, and so its connections have always to be sliced!</p>
</dd></dl>

<dl class="class">
<dt id="pybrain.structure.modules.SigmoidLayer">
<em class="property">class </em><tt class="descclassname">pybrain.structure.modules.</tt><tt class="descname">SigmoidLayer</tt><big>(</big><em>dim</em>, <em>name=None</em><big>)</big><a class="headerlink" href="#pybrain.structure.modules.SigmoidLayer" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <tt class="xref docutils literal"><span class="pre">pybrain.structure.modules.neuronlayer.NeuronLayer</span></tt></p>
<p>Layer implementing the sigmoid squashing function.</p>
<dl class="method">
<dt id="pybrain.structure.modules.SigmoidLayer.__init__">
<tt class="descname">__init__</tt><big>(</big><em>dim</em>, <em>name=None</em><big>)</big><a class="headerlink" href="#pybrain.structure.modules.SigmoidLayer.__init__" title="Permalink to this definition">¶</a></dt>
<dd>Create a layer with dim number of units.</dd></dl>

</dd></dl>

<dl class="class">
<dt id="pybrain.structure.modules.SoftmaxLayer">
<em class="property">class </em><tt class="descclassname">pybrain.structure.modules.</tt><tt class="descname">SoftmaxLayer</tt><big>(</big><em>dim</em>, <em>name=None</em><big>)</big><a class="headerlink" href="#pybrain.structure.modules.SoftmaxLayer" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <tt class="xref docutils literal"><span class="pre">pybrain.structure.modules.neuronlayer.NeuronLayer</span></tt></p>
<p>A layer implementing a softmax distribution over the input.</p>
<dl class="method">
<dt id="pybrain.structure.modules.SoftmaxLayer.__init__">
<tt class="descname">__init__</tt><big>(</big><em>dim</em>, <em>name=None</em><big>)</big><a class="headerlink" href="#pybrain.structure.modules.SoftmaxLayer.__init__" title="Permalink to this definition">¶</a></dt>
<dd>Create a layer with dim number of units.</dd></dl>

</dd></dl>

<dl class="class">
<dt id="pybrain.structure.modules.StateDependentLayer">
<em class="property">class </em><tt class="descclassname">pybrain.structure.modules.</tt><tt class="descname">StateDependentLayer</tt><big>(</big><em>dim</em>, <em>module</em>, <em>name=None</em>, <em>onesigma=True</em><big>)</big><a class="headerlink" href="#pybrain.structure.modules.StateDependentLayer" title="Permalink to this definition">¶</a></dt>
<dd>Bases: <tt class="xref docutils literal"><span class="pre">pybrain.structure.modules.neuronlayer.NeuronLayer</span></tt>, <tt class="xref docutils literal"><span class="pre">pybrain.structure.parametercontainer.ParameterContainer</span></tt></dd></dl>

<dl class="class">
<dt id="pybrain.structure.modules.TanhLayer">
<em class="property">class </em><tt class="descclassname">pybrain.structure.modules.</tt><tt class="descname">TanhLayer</tt><big>(</big><em>dim</em>, <em>name=None</em><big>)</big><a class="headerlink" href="#pybrain.structure.modules.TanhLayer" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <tt class="xref docutils literal"><span class="pre">pybrain.structure.modules.neuronlayer.NeuronLayer</span></tt></p>
<p>A layer implementing the tanh squashing function.</p>
<dl class="method">
<dt id="pybrain.structure.modules.TanhLayer.__init__">
<tt class="descname">__init__</tt><big>(</big><em>dim</em>, <em>name=None</em><big>)</big><a class="headerlink" href="#pybrain.structure.modules.TanhLayer.__init__" title="Permalink to this definition">¶</a></dt>
<dd>Create a layer with dim number of units.</dd></dl>

</dd></dl>

</div>


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